New tutorial explores deep Bayesian filtering for structural system identification

A new tutorial paper introduces deep Bayesian filtering techniques for identifying nonlinear structural systems from data. It details methods like the Deep Markov Model, Physics-guided Deep Markov Model (PgDMM), and Neural Extended Kalman Filter (Neural EKF). The paper explores how these approaches can infer system dynamics and parameters while maintaining physical structure and uncertainty awareness, demonstrated through a benchmark test case. AI

IMPACT This research could lead to more accurate and interpretable models for engineering diagnostics and prognostics.

RANK_REASON The item is a research paper published on arXiv detailing new methods in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• alphaXiv
• CatalyzeX Code Finder for Papers
• DagsHub
• Deep Bayesian Approaches for Interpretable Nonlinear Structural System Identification
• Deep Bayesian filtering
• Deep Markov Model
• Gotit.pub
• Hugging Face
• Neural EKF
• Neural Extended Kalman Filter
• PgDMM
• Physics-guided Deep Markov Model
• recursive Bayesian estimation
• ScienceCast

• paper
• model release

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